June 6, 2024, 4:43 a.m. | Yuhui Wang, Weida Li, Francesco Faccio, Qingyuan Wu, J\"urgen Schmidhuber

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.03485v1 Announce Type: new
Abstract: Value iteration networks (VINs) enable end-to-end learning for planning tasks by employing a differentiable "planning module" that approximates the value iteration algorithm. However, long-term planning remains a challenge because training very deep VINs is difficult. To address this problem, we embed highway value iteration -- a recent algorithm designed to facilitate long-term credit assignment -- into the structure of VINs. This improvement augments the "planning module" of the VIN with three additional components: 1) an …

abstract algorithm arxiv challenge cs.ai cs.lg differentiable embed however iteration long-term networks planning problem tasks training type value

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